





from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
_lowerCAmelCase								=  200
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generation and must be smaller than N_POPULATION.
_lowerCAmelCase								=  50
# Probability that an element of a generation can mutate, changing one of its genes.
# This will guarantee that all genes will be used during evolution.
_lowerCAmelCase								=  0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1_000))



def      _lowerCAmelCase		(	_lowerCAmelCase	,_lowerCAmelCase  ):
   '''simple docstring'''


   A_  :					Optional[Any]         =						len([g for position, g in enumerate(__snake_case  ) if g == main_target[position]]  )
   return (item, float(__snake_case  ))



def      _lowerCAmelCase		(	_lowerCAmelCase	,_lowerCAmelCase  ):
   '''simple docstring'''


   A_  :					Optional[Any]         =						random.randint(0	,len(__snake_case  ) - 1  )
   A_  :					Optional[int]         =						parent_a[:random_slice] + parent_a[random_slice:]
   A_  :					str         =						parent_a[:random_slice] + parent_a[random_slice:]
   return (child_a, child_a)



def      _lowerCAmelCase		(	_lowerCAmelCase	,_lowerCAmelCase  ):
   '''simple docstring'''


   A_  :					Any         =						list(__snake_case  )
   if random.uniform(0	,1  ) < MUTATION_PROBABILITY:
      A_  :					Tuple         =						random.choice(__snake_case  )
   return "".join(__snake_case  )



def      _lowerCAmelCase		(	_lowerCAmelCase	,_lowerCAmelCase	,_lowerCAmelCase	,):
   '''simple docstring'''


   A_  :					int         =						[]
   # Generate more children proportionally to the fitness score.
   A_  :					str         =						int(parent_a[1] * 1_0_0  ) + 1
   A_  :					Union[str, Any]         =						1_0 if child_n >= 1_0 else child_n
   for _ in range(__snake_case  ):
      A_  :					Tuple         =						population_score[random.randint(0	,__snake_case  )][0]

      A_   ,					A_  :					str         =						crossover(parent_a[0]	,__snake_case  )
      # Append new string to the population list.
      pop.append(mutate(__snake_case	,__snake_case  )  )
      pop.append(mutate(__snake_case	,__snake_case  )  )
   return pop



def      _lowerCAmelCase		(	_lowerCAmelCase	,_lowerCAmelCase	,_lowerCAmelCase = True  ):
   '''simple docstring'''


   if N_POPULATION < N_SELECTED:
      A_  :					Optional[Any]         =						f"""{N_POPULATION} must be bigger than {N_SELECTED}"""
      raise ValueError(__snake_case  )
   # Verify that the target contains no genes besides the ones inside genes variable.
   A_  :					Union[str, Any]         =						sorted({c for c in target if c not in genes}  )
   if not_in_genes_list:
      A_  :					Optional[int]         =						f"""{not_in_genes_list} is not in genes list, evolution cannot converge"""
      raise ValueError(__snake_case  )

   # Generate random starting population.
   A_  :					List[str]         =						[]
   for _ in range(__snake_case  ):
      population.append("""""".join([random.choice(__snake_case  ) for i in range(len(__snake_case  )  )]  )  )

   # Just some logs to know what the algorithms is doing.
   A_   ,					A_  :					List[Any]         =						0, 0

   # This loop will end when we find a perfect match for our target.
   while True:
      generation += 1
      total_population += len(__snake_case  )

      # Random population created. Now it's time to evaluate.

      # Adding a bit of concurrency can make everything faster,
      #
      # import concurrent.futures
      # population_score: list[tuple[str, float]] = []
      # with concurrent.futures.ThreadPoolExecutor(
      #                                   max_workers=NUM_WORKERS) as executor:
      #     futures = {executor.submit(evaluate, item) for item in population}
      #     concurrent.futures.wait(futures)
      #     population_score = [item.result() for item in futures]
      #
      # but with a simple algorithm like this, it will probably be slower.
      # We just need to call evaluate for every item inside the population.
      A_  :					List[Any]         =						[evaluate(__snake_case	,__snake_case  ) for item in population]

      # Check if there is a matching evolution.
      A_  :					Union[str, Any]         =						sorted(__snake_case	,key=lambda _lowerCAmelCase  : x[1]	,reverse=__snake_case  )
      if population_score[0][0] == target:
         return (generation, total_population, population_score[0][0])

      # Print the best result every 10 generation.
      # Just to know that the algorithm is working.
      if debug and generation % 1_0 == 0:
         print(
             f"""\nGeneration: {generation}"""
             f"""\nTotal Population:{total_population}"""
             f"""\nBest score: {population_score[0][1]}"""
             f"""\nBest string: {population_score[0][0]}"""  )

      # Flush the old population, keeping some of the best evolutions.
      # Keeping this avoid regression of evolution.
      A_  :					Dict         =						population[: int(N_POPULATION / 3  )]
      population.clear()
      population.extend(__snake_case  )
      # Normalize population score to be between 0 and 1.
      A_  :					Dict         =						[
          (item, score / len(__snake_case  )) for item, score in population_score
      ]

      # This is selection
      for i in range(__snake_case  ):
         population.extend(select(population_score[int(__snake_case  )]	,__snake_case	,__snake_case  )  )
         # Check if the population has already reached the maximum value and if so,
         # break the cycle.  If this check is disabled, the algorithm will take
         # forever to compute large strings, but will also calculate small strings in
         # a far fewer generations.
         if len(__snake_case  ) > N_POPULATION:
            break


if __name__ == "__main__":
      _lowerCAmelCase								=  (
          """This is a genetic algorithm to evaluate, combine, evolve, and mutate a string!"""
      )
      _lowerCAmelCase								=  list(
          """ ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklm"""
          """nopqrstuvwxyz.,;!?+-*#@^'èéòà€ù=)(&%$£/\\"""
      )
      _lowerCAmelCase								=  basic(target_str, genes_list)
      print(
          f'''\nGeneration: {generation}\nTotal Population: {population}\nTarget: {target}'''
      )














"""simple docstring"""
import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_lxmert import LxmertTokenizer


snake_case : List[Any]     		=		{"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

snake_case : List[Any]     		=		{
    """vocab_file""": {
        """unc-nlp/lxmert-base-uncased""": """https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/vocab.txt""",
    },
    """tokenizer_file""": {
        """unc-nlp/lxmert-base-uncased""": (
            """https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/tokenizer.json"""
        ),
    },
}

snake_case : Optional[int]     		=		{
    """unc-nlp/lxmert-base-uncased""": 5_1_2,
}

snake_case : List[Any]     		=		{
    """unc-nlp/lxmert-base-uncased""": {"""do_lower_case""": True},
}



class        UpperCamelCase__ (				a_):







    """simple docstring"""
    __UpperCAmelCase           = VOCAB_FILES_NAMES
    __UpperCAmelCase           = PRETRAINED_VOCAB_FILES_MAP
    __UpperCAmelCase           = PRETRAINED_INIT_CONFIGURATION
    __UpperCAmelCase           = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __UpperCAmelCase           = LxmertTokenizer







    def __init__(		self :	Union[str, Any]				,    UpperCamelCase_ :	List[str]=None				,    UpperCamelCase_ :	Dict=None				,    UpperCamelCase_ :	Optional[int]=True				,    UpperCamelCase_ :	Dict="[UNK]"				,    UpperCamelCase_ :	str="[SEP]"				,    UpperCamelCase_ :	Dict="[PAD]"				,    UpperCamelCase_ :	Optional[int]="[CLS]"				,    UpperCamelCase_ :	Any="[MASK]"				,    UpperCamelCase_ :	Any=True				,    UpperCamelCase_ :	List[Any]=None				,    **UpperCamelCase_ :	Dict				,    ):





           '''simple docstring'''

           super().__init__(
               UpperCamelCase_				,    tokenizer_file=UpperCamelCase_				,    do_lower_case=UpperCamelCase_				,    unk_token=UpperCamelCase_				,    sep_token=UpperCamelCase_				,    pad_token=UpperCamelCase_				,    cls_token=UpperCamelCase_				,    mask_token=UpperCamelCase_				,    tokenize_chinese_chars=UpperCamelCase_				,    strip_accents=UpperCamelCase_				,    **UpperCamelCase_				,    )

           __magic_name__      				=    json.loads(self.backend_tokenizer.normalizer.__getstate__() )
           if (
               normalizer_state.get('lowercase'				,    UpperCamelCase_ ) != do_lower_case
               or normalizer_state.get('strip_accents'				,    UpperCamelCase_ ) != strip_accents
               or normalizer_state.get('handle_chinese_chars'				,    UpperCamelCase_ ) != tokenize_chinese_chars
           ):
                  __magic_name__      				=    getattr(UpperCamelCase_				,    normalizer_state.pop('type' ) )
                  __magic_name__      				=    do_lower_case
                  __magic_name__      				=    strip_accents
                  __magic_name__      				=    tokenize_chinese_chars
                  __magic_name__      				=    normalizer_class(**UpperCamelCase_ )

           __magic_name__      				=    do_lower_case







    def 				a__			(		self :	Dict				,    UpperCamelCase_ :	Optional[Any]				,    UpperCamelCase_ :	Optional[int]=None ):





           '''simple docstring'''

           __magic_name__      				=    [self.cls_token_id] + token_ids_a + [self.sep_token_id]

           if token_ids_a:
                  output += token_ids_a + [self.sep_token_id]

           return output







    def 				a__			(		self :	int				,    UpperCamelCase_ :	List[int]				,    UpperCamelCase_ :	Optional[List[int]] = None ):





           '''simple docstring'''

           __magic_name__      				=    [self.sep_token_id]
           __magic_name__      				=    [self.cls_token_id]
           if token_ids_a is None:
                  return len(cls + token_ids_a + sep ) * [0]
           return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]







    def 				a__			(		self :	Dict				,    UpperCamelCase_ :	str				,    UpperCamelCase_ :	Optional[str] = None ):





           '''simple docstring'''

           __magic_name__      				=    self._tokenizer.model.save(UpperCamelCase_				,    name=UpperCamelCase_ )
           return tuple(UpperCamelCase_ )